Synthetic Data
Artificially generated data that mimics real data properties, used when real data is scarce, sensitive, or biased.
Definition
Synthetic data is generated algorithmically rather than collected from the real world. It is used to: augment limited training datasets, protect privacy (replacing sensitive real data), generate rare events (fraud, accidents) at scale, and create balanced datasets free from historical biases.
Generation methods include rule-based simulation, GANs (generative adversarial networks), diffusion models, and LLM-based generation for text. For vision AI, rendering engines (Unreal Engine, NVIDIA Omniverse) generate photorealistic synthetic training images with perfect annotations.
Synthetic data has limitations: distribution mismatch with real data can cause models to perform well in training but poorly in deployment ("sim-to-real gap"). Careful validation against real-world performance is essential. Anthropic and other labs use synthetic data generated by frontier models to train smaller models (a form of model distillation).
Examples
- NVIDIA Omniverse for robot training
- Stability AI synthetic image augmentation
- LLM-generated instruction datasets
Companies using this
Related Terms
Data Labeling / Annotation
The process of tagging raw data with labels, bounding boxes, or other metadata to create supervised learning training datasets.
GAN (Generative Adversarial Network)
A generative model architecture where two networks — a generator and discriminator — compete to produce realistic synthetic data.
Machine Learning (ML)
A subset of AI where systems learn patterns from data rather than following explicitly programmed rules.